Clinical Procedures for the Prevention of Preeclampsia in Pregnant Women: A Systematic Review
Bibliographic record
Abstract
Abstract Objective To identify the most effective procedures recommended for the prevention of preeclampsia. Data Sources A systematic review was performed in the following databases: Pubmed/MEDLINE, CINAHL, Web of Science, Cochrane and LILACS via the Virtual Health Library (VHL). A manual search was also performed to find additional references. The risk of bias, the quality of the evidence, and the classification of the strength of the recommendations were evaluated using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Selection of Studies In the initial search in the databases, the total number of articles retrieved was 351, and 2 were retrieved through the manual search; after duplicate articles were removed, 333 citations remained. After a thorough review of the titles and abstracts, 315 references were excluded. Accordingly, 18 articles were maintained for selection of the complete text (phase 2). This process led to the exclusion of 6 studies. In total, 12 articles were selected for data extraction and qualitative synthesis. Data Collection The articles selected for the study were analyzed, and we inserted the synthesis of the evidence in the online software GRADEpro Guideline Development Tool (GDT) (McMaster University and Evidence Prime Inc. All right reserved. McMaster University, Hamilton, Ontário, Canada); thus, it was possible to develop a table of evidence, with the quality of the evidence and the classification of the strength of the recommendations. Data Synthesis In total, seven studies recommended the individual use of aspirin, or aspirin combined with calcium, heparin or dipyridamole. The use of calcium alone or in combination with phytonutrients was also highlighted. All of the studies were with women at a high risk of developing preeclampsia. Conclusion According to the studies evaluated, the administration of aspirin is still the best procedure to be used in the clinical practice to prevent preeclampsia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".